The present invention relates to surface-based object identification.
Machine learning, such as in neural networks, has been used for image identification of objects. For example, nodule (or cancer) detection based on CT images is known. In the past, 2D images were input into a neural network for nodule detection, as disclosed by Hua, Kai-Lung et al., in “Computer-Aided Classification of Lung Nodules on Computed Tomography Images via Deep Learning Technique”, published by OncoTargets and therapy 8 (2015): 2015-2022. PMC. Web. 17 Oct. 2016, and by Richard Gruetzemacher and Ashish Gupta, in “Using Deep Learning for Pulmonary Nodule Detection & Diagnosis”, published by Intelligence and Intelligent Systems (SIGODIS), 2016.11.8.
However, since the 2D (two-dimensional) images may sometimes not represent 3D (three-dimensional) morphological features of an object, the identification of the object with the 2D images may not be sufficiently precise. In addition, the 2D images require long training times due to their large data size.
Instead of the 2D images, 3D data of objects may be used for identification of the objects. Nevertheless, the training with the 3D data is more time consuming than with the 2D images.